Surveillance systems today provide a whole new level of pro-active control and monitoring. Network video technology not only offers superior loss prevention, but it can also be used to boost sales, improve staff and customer security, optimize store layouts, boost productivity, count people for statistical purposes, monitor flow control, and to improve many more key functions.
For instance, today's retail industry faces though challenges. Theft and inventory shrinkage are obvious causes of lost profits that are traditionally fought with surveillance systems. Also, retail surveillance offers instant benefits to businesses not only by helping to win the battle of protecting such businesses, but also by preventing crime and by making major positive contributions to planning and maximizing profits.
Unfortunately, with increased volumes of shoppers and in-store employees, theft is growing at an alarming rate. In an attempt to detect such theft, many variations of in-store surveillance systems are implemented. Data gathered by such systems is often analyzed and, based on such analysis, further actions are determined. Many of such systems will benefit greatly if such collected data is classified and formatted. For instance, there is an obvious advantage in performing automatic people counting by overhead video cameras.
There are several previously proposed and developed solutions dealing with people-counting using television technologies. Most of such solutions are based on blob tracking by overhead cameras. However, there is a well known draw back to such blob-based person tracking—it is sensitive to shadows which makes such solutions less stable and unusable for over-crowded areas.
Thus, there exist a need for providing a method and a system for accurate detection and counting of people using overhead camera views such method comprising: generating a set of person-shape models during a cumulative training process; detecting persons in a camera field-of-view by using said set of person-shape models, and counting people by tracking detected persons upon crossing by said detected persons of a previously established virtual boundary.